Overview

Dataset statistics

Number of variables11
Number of observations193
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.0 KiB
Average record size in memory84.7 B

Variable types

Numeric11

Alerts

population is highly correlated with GDP_constant_2010_USD and 4 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 6 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 2 other fieldsHigh correlation
population_% is highly correlated with population and 4 other fieldsHigh correlation
GDP_% is highly correlated with population and 6 other fieldsHigh correlation
land_area_% is highly correlated with population and 2 other fieldsHigh correlation
CC.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
PV.EST is highly correlated with CC.EST and 3 other fieldsHigh correlation
RQ.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
VA.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 2 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 3 other fieldsHigh correlation
land_area_km_sq is highly correlated with land_area_%High correlation
population_% is highly correlated with population and 2 other fieldsHigh correlation
GDP_% is highly correlated with population and 3 other fieldsHigh correlation
land_area_% is highly correlated with land_area_km_sqHigh correlation
CC.EST is highly correlated with GE.EST and 3 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
PV.EST is highly correlated with CC.EST and 3 other fieldsHigh correlation
RQ.EST is highly correlated with CC.EST and 3 other fieldsHigh correlation
VA.EST is highly correlated with CC.EST and 3 other fieldsHigh correlation
population is highly correlated with land_area_km_sq and 2 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with GDP_%High correlation
land_area_km_sq is highly correlated with population and 2 other fieldsHigh correlation
population_% is highly correlated with population and 2 other fieldsHigh correlation
GDP_% is highly correlated with GDP_constant_2010_USDHigh correlation
land_area_% is highly correlated with population and 2 other fieldsHigh correlation
CC.EST is highly correlated with GE.EST and 2 other fieldsHigh correlation
GE.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
RQ.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
VA.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 6 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 8 other fieldsHigh correlation
population_% is highly correlated with population and 8 other fieldsHigh correlation
GDP_% is highly correlated with population and 8 other fieldsHigh correlation
land_area_% is highly correlated with population and 9 other fieldsHigh correlation
CC.EST is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
PV.EST is highly correlated with population and 7 other fieldsHigh correlation
RQ.EST is highly correlated with population and 8 other fieldsHigh correlation
VA.EST is highly correlated with population and 8 other fieldsHigh correlation
population has unique values Unique
GDP_constant_2010_USD has unique values Unique
population_% has unique values Unique
GDP_% has unique values Unique
land_area_% has unique values Unique
CC.EST has unique values Unique
GE.EST has unique values Unique
PV.EST has unique values Unique
RQ.EST has unique values Unique
VA.EST has unique values Unique

Reproduction

Analysis started2023-01-05 14:02:46.483680
Analysis finished2023-01-05 14:02:53.383022
Duration6.9 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27271809.02
Minimum423196
Maximum127540423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-01-05T15:02:53.416448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum423196
5-th percentile2381023
Q17995062
median15577894
Q343847430
95-th percentile77034109.8
Maximum127540423
Range127117227
Interquartile range (IQR)35852368

Descriptive statistics

Standard deviation26518943.34
Coefficient of variation (CV)0.9723939957
Kurtosis1.330292092
Mean27271809.02
Median Absolute Deviation (MAD)11938302
Skewness1.30889498
Sum5263459140
Variance7.032543558 × 1014
MonotonicityNot monotonic
2023-01-05T15:02:53.488373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114032481
 
0.5%
107099731
 
0.5%
226190041
 
0.5%
595397171
 
0.5%
98179581
 
0.5%
13625501
 
0.5%
97301461
 
0.5%
627048971
 
0.5%
37193001
 
0.5%
29248161
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
4231961
0.5%
11350621
0.5%
13164811
0.5%
13226961
0.5%
13370901
0.5%
13625501
0.5%
13969851
0.5%
17568171
0.5%
20348191
0.5%
21095681
0.5%
ValueCountFrequency (%)
1275404231
0.5%
1208283071
0.5%
1024031961
0.5%
1017196731
0.5%
956886811
0.5%
956874521
0.5%
924441831
0.5%
878132571
0.5%
809538811
0.5%
795124261
0.5%

GDP_constant_2010_USD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.735368762 × 1011
Minimum1444065193
Maximum2.81053 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-01-05T15:02:53.558229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1444065193
5-th percentile4663192663
Q12.619918998 × 1010
median1.09983 × 1011
Q33.66899 × 1011
95-th percentile2.079564 × 1012
Maximum2.81053 × 1012
Range2.809085935 × 1012
Interquartile range (IQR)3.4069981 × 1011

Descriptive statistics

Standard deviation6.288656301 × 1011
Coefficient of variation (CV)1.683543634
Kurtosis5.254488877
Mean3.735368762 × 1011
Median Absolute Deviation (MAD)9.671150574 × 1010
Skewness2.417499193
Sum7.20926171 × 1013
Variance3.954719807 × 1023
MonotonicityNot monotonic
2023-01-05T15:02:53.622222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.863909459 × 10101
 
0.5%
4.81481 × 10111
 
0.5%
1.15715 × 10111
 
0.5%
2.07706 × 10121
 
0.5%
1.47183 × 10111
 
0.5%
1.818513502 × 10101
 
0.5%
3.678614251 × 10101
 
0.5%
2.50689 × 10121
 
0.5%
1.518961435 × 10101
 
0.5%
1.150199867 × 10101
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
14440651931
0.5%
19100000001
0.5%
21455319091
0.5%
22972413921
0.5%
32050834281
0.5%
34098808911
0.5%
35241739021
0.5%
35543622041
0.5%
37598326291
0.5%
46350732161
0.5%
ValueCountFrequency (%)
2.81053 × 10121
0.5%
2.75379 × 10121
0.5%
2.70681 × 10121
0.5%
2.67448 × 10121
0.5%
2.50689 × 10121
0.5%
2.50542 × 10121
0.5%
2.34648 × 10121
0.5%
2.21102 × 10121
0.5%
2.09521 × 10121
0.5%
2.08332 × 10121
0.5%

land_area_km_sq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct100
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543535.0207
Minimum1050
Maximum7682300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size900.0 B
2023-01-05T15:02:53.753214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1050
5-th percentile21640
Q169490
median229520
Q3547566
95-th percentile2699700
Maximum7682300
Range7681250
Interquartile range (IQR)478076

Descriptive statistics

Standard deviation989768.9048
Coefficient of variation (CV)1.820984605
Kurtosis27.52914833
Mean543535.0207
Median Absolute Deviation (MAD)196590
Skewness4.573542027
Sum104902259
Variance9.796424849 × 1011
MonotonicityNot monotonic
2023-01-05T15:02:53.822917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26997006
 
3.1%
27366905
 
2.6%
4463005
 
2.6%
216405
 
2.6%
5691405
 
2.6%
423905
 
2.6%
19439504
 
2.1%
4727104
 
2.1%
284704
 
2.1%
302804
 
2.1%
Other values (90)146
75.6%
ValueCountFrequency (%)
10501
 
0.5%
52701
 
0.5%
92401
 
0.5%
108303
1.6%
116101
 
0.5%
216405
2.6%
254301
 
0.5%
256802
 
1.0%
284704
2.1%
302804
2.1%
ValueCountFrequency (%)
76823002
 
1.0%
27366905
2.6%
26997006
3.1%
19439504
2.1%
11095004
2.1%
10000004
2.1%
9954504
2.1%
8820503
1.6%
7863802
 
1.0%
7696303
1.6%

population_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004089144504
Minimum5.637205473 × 10-5
Maximum0.01698909183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-01-05T15:02:53.897246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.637205473 × 10-5
5-th percentile0.000363100301
Q10.001138521132
median0.002441875759
Q30.006480905299
95-th percentile0.01166147313
Maximum0.01698909183
Range0.01693271978
Interquartile range (IQR)0.005342384167

Descriptive statistics

Standard deviation0.003904629065
Coefficient of variation (CV)0.9548767626
Kurtosis0.9512212337
Mean0.004089144504
Median Absolute Deviation (MAD)0.001814551456
Skewness1.223473731
Sum0.7892048892
Variance1.524612814 × 10-5
MonotonicityNot monotonic
2023-01-05T15:02:53.961601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0015189758891
 
0.5%
0.001586473821
 
0.5%
0.0039008692521
 
0.5%
0.0083662139491
 
0.5%
0.0013078064671
 
0.5%
0.00021206313941
 
0.5%
0.001514370341
 
0.5%
0.0097591995251
 
0.5%
0.00049543139151
 
0.5%
0.00038960171561
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
5.637205473 × 10-51
0.5%
0.0001594930581
0.5%
0.00017536257191
0.5%
0.00018585841991
0.5%
0.00019806383081
0.5%
0.00021206313941
0.5%
0.00022919574891
0.5%
0.00024685886381
0.5%
0.0002964256151
0.5%
0.00033384171241
0.5%
ValueCountFrequency (%)
0.016989091831
0.5%
0.016978170511
0.5%
0.016688594821
0.5%
0.01650224031
0.5%
0.013640673751
0.5%
0.01298977981
0.5%
0.012746263111
0.5%
0.012339065971
0.5%
0.011991740111
0.5%
0.011732209571
0.5%

GDP_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006138297074
Minimum2.969087104 × 10-5
Maximum0.04705420302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-01-05T15:02:54.031365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.969087104 × 10-5
5-th percentile8.395039538 × 10-5
Q10.0004846231143
median0.001902277875
Q30.006366334483
95-th percentile0.03489236416
Maximum0.04705420302
Range0.04702451215
Interquartile range (IQR)0.005881711369

Descriptive statistics

Standard deviation0.01035162526
Coefficient of variation (CV)1.686400175
Kurtosis5.61102679
Mean0.006138297074
Median Absolute Deviation (MAD)0.001620243898
Skewness2.467149028
Sum1.184691335
Variance0.0001071561455
MonotonicityNot monotonic
2023-01-05T15:02:54.098616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00062863967641
 
0.5%
0.0074967999991
 
0.5%
0.0026414516441
 
0.5%
0.029719103161
 
0.5%
0.0019022778751
 
0.5%
0.00032525370691
 
0.5%
0.00065794558011
 
0.5%
0.044837460061
 
0.5%
0.00019631932561
 
0.5%
0.00014865845631
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
2.969087104 × 10-51
0.5%
3.296399238 × 10-51
0.5%
3.837432088 × 10-51
0.5%
4.359998824 × 10-51
0.5%
6.401400119 × 10-51
0.5%
7.038708272 × 10-51
0.5%
7.289915905 × 10-51
0.5%
7.783809778 × 10-51
0.5%
7.946879576 × 10-51
0.5%
8.113620435 × 10-51
0.5%
ValueCountFrequency (%)
0.047054203021
0.5%
0.046865417671
0.5%
0.044837460061
0.5%
0.043148009311
0.5%
0.041846890561
0.5%
0.041642435861
0.5%
0.039010122221
0.5%
0.038729726451
0.5%
0.036324908691
0.5%
0.035591568241
0.5%

land_area_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004166567797
Minimum8.010714827 × 10-6
Maximum0.0586102043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-01-05T15:02:54.165466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8.010714827 × 10-6
5-th percentile0.0001674664493
Q10.0005377720976
median0.001751065969
Q30.004237325189
95-th percentile0.02066399999
Maximum0.0586102043
Range0.05860219359
Interquartile range (IQR)0.003699553091

Descriptive statistics

Standard deviation0.007553740985
Coefficient of variation (CV)1.812940855
Kurtosis27.06748635
Mean0.004166567797
Median Absolute Deviation (MAD)0.001499834694
Skewness4.533740153
Sum0.8041475849
Variance5.705900287 × 10-5
MonotonicityNot monotonic
2023-01-05T15:02:54.231282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0011862904531
 
0.5%
0.00023432484061
 
0.5%
0.0017510659691
 
0.5%
0.002228993251
 
0.5%
0.00069126464151
 
0.5%
0.00032804949221
 
0.5%
0.001569514451
 
0.5%
0.0042375342371
 
0.5%
0.00053060841641
 
0.5%
0.00021738986351
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
8.010714827 × 10-61
0.5%
4.024041379 × 10-51
0.5%
7.002073038 × 10-51
0.5%
8.206975217 × 10-51
0.5%
8.26248015 × 10-51
0.5%
8.380904965 × 10-51
0.5%
8.798059304 × 10-51
0.5%
0.0001650970181
0.5%
0.00016523767641
0.5%
0.00016746332731
0.5%
ValueCountFrequency (%)
0.05861020431
0.5%
0.058216478031
0.5%
0.021178810211
0.5%
0.021178152181
0.5%
0.020992141631
0.5%
0.020896686531
0.5%
0.020892550471
0.5%
0.020891901321
0.5%
0.020878898251
0.5%
0.020738639891
0.5%

CC.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.006075026477
Minimum-1.527264118
Maximum2.211410761
Zeros0
Zeros (%)0.0%
Negative110
Negative (%)57.0%
Memory size1.6 KiB
2023-01-05T15:02:54.305326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.527264118
5-th percentile-1.259118152
Q1-0.752358675
median-0.2335734367
Q30.6832193732
95-th percentile1.739721608
Maximum2.211410761
Range3.738674879
Interquartile range (IQR)1.435578048

Descriptive statistics

Standard deviation0.9571424146
Coefficient of variation (CV)-157.5536203
Kurtosis-0.6927307493
Mean-0.006075026477
Median Absolute Deviation (MAD)0.64142102
Skewness0.5518472929
Sum-1.17248011
Variance0.9161216018
MonotonicityNot monotonic
2023-01-05T15:02:54.369609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1377614291
 
0.5%
1.362637521
 
0.5%
-0.44604268671
 
0.5%
0.17201064531
 
0.5%
0.099874198441
 
0.5%
0.98284685611
 
0.5%
-0.75929254291
 
0.5%
1.3111637831
 
0.5%
0.74646264311
 
0.5%
-0.63774907591
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
-1.5272641181
0.5%
-1.5184663531
0.5%
-1.3858449461
0.5%
-1.3503448961
0.5%
-1.325292111
0.5%
-1.2923827171
0.5%
-1.2870285511
0.5%
-1.2803416251
0.5%
-1.272439481
0.5%
-1.2603490351
0.5%
ValueCountFrequency (%)
2.2114107611
0.5%
2.2068724631
0.5%
2.1739861971
0.5%
2.0724573141
0.5%
2.0266244411
0.5%
1.9775072341
0.5%
1.8904258011
0.5%
1.8773558141
0.5%
1.8287581211
0.5%
1.7510586981
0.5%

GE.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1430007275
Minimum-2.025118113
Maximum1.94633615
Zeros0
Zeros (%)0.0%
Negative96
Negative (%)49.7%
Memory size1.6 KiB
2023-01-05T15:02:54.441676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.025118113
5-th percentile-1.178235483
Q1-0.6200786233
median0.0002336448088
Q30.908039093
95-th percentile1.701452947
Maximum1.94633615
Range3.971454263
Interquartile range (IQR)1.528117716

Descriptive statistics

Standard deviation0.925441407
Coefficient of variation (CV)6.471585308
Kurtosis-0.854008612
Mean0.1430007275
Median Absolute Deviation (MAD)0.6875932271
Skewness0.1640323227
Sum27.5991404
Variance0.8564417977
MonotonicityNot monotonic
2023-01-05T15:02:54.567021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.17324103421
 
0.5%
1.3806594611
 
0.5%
-0.28693386911
 
0.5%
0.44723397491
 
0.5%
0.49861377481
 
0.5%
0.9080390931
 
0.5%
-1.2076408861
 
0.5%
1.7551865581
 
0.5%
0.45859897141
 
0.5%
-0.27509307861
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
-2.0251181131
0.5%
-1.9084531071
0.5%
-1.7478058341
0.5%
-1.6624231341
0.5%
-1.4559838771
0.5%
-1.236603261
0.5%
-1.2076408861
0.5%
-1.2065011261
0.5%
-1.200643421
0.5%
-1.1949434281
0.5%
ValueCountFrequency (%)
1.946336151
0.5%
1.9219168421
0.5%
1.8472652441
0.5%
1.8433197741
0.5%
1.8005645281
0.5%
1.7710525991
0.5%
1.7688223121
0.5%
1.7551865581
0.5%
1.7316496371
0.5%
1.7249964481
0.5%

PV.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08530244362
Minimum-2.271226406
Maximum1.456821561
Zeros0
Zeros (%)0.0%
Negative96
Negative (%)49.7%
Memory size1.6 KiB
2023-01-05T15:02:54.638174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.271226406
5-th percentile-1.444824624
Q1-0.6624541879
median0.008457476273
Q30.547544241
95-th percentile1.128926396
Maximum1.456821561
Range3.728047967
Interquartile range (IQR)1.209998429

Descriptive statistics

Standard deviation0.8273302789
Coefficient of variation (CV)-9.698787558
Kurtosis-0.5666611049
Mean-0.08530244362
Median Absolute Deviation (MAD)0.6181809958
Skewness-0.3199039023
Sum-16.46337162
Variance0.6844753903
MonotonicityNot monotonic
2023-01-05T15:02:54.702495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.1413267851
 
0.5%
0.62057119611
 
0.5%
0.59796673061
 
0.5%
0.50821751361
 
0.5%
0.65467864271
 
0.5%
0.70392620561
 
0.5%
0.16176687181
 
0.5%
0.3451153041
 
0.5%
-0.31329149011
 
0.5%
-0.66226381061
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
-2.2712264061
0.5%
-2.116748811
0.5%
-2.0090630051
0.5%
-1.9707207681
0.5%
-1.8534623381
0.5%
-1.6198132041
0.5%
-1.5705785751
0.5%
-1.5642713311
0.5%
-1.5309436321
0.5%
-1.4555166961
0.5%
ValueCountFrequency (%)
1.4568215611
0.5%
1.4127187731
0.5%
1.4104628561
0.5%
1.396112681
0.5%
1.3434536461
0.5%
1.3392705921
0.5%
1.2235988381
0.5%
1.1541720631
0.5%
1.1477580071
0.5%
1.1298111681
0.5%

RQ.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1427874821
Minimum-2.526690006
Maximum1.805724502
Zeros0
Zeros (%)0.0%
Negative88
Negative (%)45.6%
Memory size1.6 KiB
2023-01-05T15:02:54.776888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.526690006
5-th percentile-1.585124826
Q1-0.4436825216
median0.1417089403
Q31.042177677
95-th percentile1.580911899
Maximum1.805724502
Range4.332414508
Interquartile range (IQR)1.485860199

Descriptive statistics

Standard deviation0.9868060147
Coefficient of variation (CV)6.911012087
Kurtosis-0.5723187068
Mean0.1427874821
Median Absolute Deviation (MAD)0.7732278407
Skewness-0.3447287622
Sum27.55798405
Variance0.9737861106
MonotonicityNot monotonic
2023-01-05T15:02:54.839439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.44368252161
 
0.5%
1.4056702851
 
0.5%
0.026118133221
 
0.5%
0.74457830191
 
0.5%
0.59968668221
 
0.5%
1.2255069021
 
0.5%
-1.1903620961
 
0.5%
1.216464521
 
0.5%
0.86886698011
 
0.5%
0.21440464261
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
-2.5266900061
0.5%
-2.2713718411
0.5%
-2.2429056171
0.5%
-2.1858844761
0.5%
-1.9864451891
0.5%
-1.7767726181
0.5%
-1.6414649491
0.5%
-1.6279963251
0.5%
-1.602391721
0.5%
-1.5910269021
0.5%
ValueCountFrequency (%)
1.8057245021
0.5%
1.7852679491
0.5%
1.7788971661
0.5%
1.7576309441
0.5%
1.7378761771
0.5%
1.6950287821
0.5%
1.6777950531
0.5%
1.6587121491
0.5%
1.6358840471
0.5%
1.606451751
0.5%

VA.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct193
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002967606656
Minimum-2.240076303
Maximum1.520166636
Zeros0
Zeros (%)0.0%
Negative92
Negative (%)47.7%
Memory size1.6 KiB
2023-01-05T15:02:54.904962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.240076303
5-th percentile-1.636005592
Q1-0.906963706
median0.1127453446
Q30.9657084346
95-th percentile1.370710754
Maximum1.520166636
Range3.760242939
Interquartile range (IQR)1.872672141

Descriptive statistics

Standard deviation1.02571662
Coefficient of variation (CV)-345.6376598
Kurtosis-1.146464286
Mean-0.002967606656
Median Absolute Deviation (MAD)0.9192784429
Skewness-0.2474297936
Sum-0.5727480846
Variance1.052094584
MonotonicityNot monotonic
2023-01-05T15:02:54.969843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.30374932291
 
0.5%
1.3249884841
 
0.5%
0.28602337841
 
0.5%
0.91680926081
 
0.5%
0.40205392241
 
0.5%
1.0934190751
 
0.5%
-1.5973640681
 
0.5%
1.456458331
 
0.5%
0.29523363711
 
0.5%
-0.57924234871
 
0.5%
Other values (183)183
94.8%
ValueCountFrequency (%)
-2.2400763031
0.5%
-2.226342441
0.5%
-2.068298341
0.5%
-1.9070141321
0.5%
-1.8867889641
0.5%
-1.8333867791
0.5%
-1.8006389141
0.5%
-1.7855881451
0.5%
-1.780859471
0.5%
-1.689458371
0.5%
ValueCountFrequency (%)
1.5201666361
0.5%
1.5183906561
0.5%
1.517697931
0.5%
1.4991903311
0.5%
1.4756511451
0.5%
1.456458331
0.5%
1.4489500521
0.5%
1.4402029511
0.5%
1.3824369911
0.5%
1.377020241
0.5%

Interactions

2023-01-05T15:02:52.585369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.663602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.222227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.848430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.424782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.073652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.606897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.225724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.848921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.425828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.059028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.640337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.715172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.272958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.899017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.476564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.122405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.719306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.275950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.901691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.477690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.108751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.750297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.766307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.321964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.949059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.528144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.169679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.767790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.325684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.951984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.527550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.154682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.803325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.818912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.375006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.004354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.586412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.221354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.821476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.377997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.006902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.580629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.204119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.857156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.871528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.426826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.057866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.706376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.272367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.873845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.431564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.062142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.634256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.253627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.905259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.917957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.472557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.107416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.756599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.317155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.920774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.479133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.113802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.742697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.298564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.957834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:46.969109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.522576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.159665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.808843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.365142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.971588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.530467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.166220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.798626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.346384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:53.008160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.020551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.573941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.211716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.863141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.414965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.023397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.582341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.217879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.850495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.396305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:53.061860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.072942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.689708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.270470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.916763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.465626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.076011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.635326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.272788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.907775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.445085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:53.114557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.123549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.739303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.323922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.969032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.514398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.127439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.751350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.325760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.958339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.493900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:53.161148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.169540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:47.786930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:48.371802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.019220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:49.558731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.173763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:50.796817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:51.373118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.004789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-05T15:02:52.537143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-01-05T15:02:55.034232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-05T15:02:55.125435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-05T15:02:55.218403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-05T15:02:55.306823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-05T15:02:53.242769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-05T15:02:53.348783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

populationGDP_constant_2010_USDland_area_km_sqpopulation_%GDP_%land_area_%CC.ESTGE.ESTPV.ESTRQ.ESTVA.EST
011403248.04.863909e+101553600.0015190.0006290.001186-0.137761-0.173241-1.141327-0.4436830.303749
18171966.03.598000e+11825800.0012720.0064350.0006392.0266241.8433201.0896491.5141031.475651
246881475.03.183030e+1111095000.0065880.0045540.008408-0.4222980.000234-1.4018100.326595-0.063991
311266941.04.449707e+101064100.0017540.0007960.0008230.217612-0.4678690.355422-1.602392-1.785588
431568179.03.306633e+118820500.0042050.0042740.006735-1.350345-1.236603-1.024604-1.986445-1.135423
524335146.02.966076e+101204100.0036050.0004620.000932-1.518466-2.0251180.524418-2.185884-2.226342
610524117.02.297241e+09256800.0014020.0000300.000196-1.260349-1.455984-1.970721-0.844684-1.555478
73198231.04.287289e+10626750.0004740.0006680.0004850.1125520.5613110.7467651.0421780.849656
84898400.03.205083e+091918000.0008040.0000640.001491-1.007479-0.405963-0.182535-0.234089-1.094260
98763400.04.611390e+10826270.0012980.0007180.000639-1.156654-0.784415-0.330350-0.367752-1.325132

Last rows

populationGDP_constant_2010_USDland_area_km_sqpopulation_%GDP_%land_area_%CC.ESTGE.ESTPV.ESTRQ.ESTVA.EST
1833639592.03.524174e+09328800.0005970.0000700.000256-0.623196-0.535638-0.428261-0.137222-0.143778
18466896109.02.810530e+125475570.0089110.0363250.0041811.3646441.410836-0.0953591.0668531.136472
1857995062.06.514177e+091399600.0011230.0000930.001061-1.280342-0.934514-1.240860-1.011845-1.422404
18664374990.02.674480e+125475570.0095360.0416420.0042371.3950351.5734710.5437591.2842531.304794
1878734951.08.458801e+091387860.0011640.0001090.001060-1.148092-1.041009-0.777760-1.102454-1.689458
1885124573.01.694140e+113045900.0008840.0038670.0023242.2068721.7249961.4127191.5481611.517698
1894586897.02.286620e+11688900.0006450.0032720.0005221.4557001.5387580.9410731.5638851.322521
1908048600.01.314654e+10826050.0013200.0002630.000642-1.292383-0.951395-0.829795-0.859867-0.906229
19125864350.02.437855e+104254000.0040250.0004360.003292-1.068812-1.194943-1.455517-1.581190-1.780859
1928306500.01.958171e+10826720.0012930.0003500.000640-1.180832-0.930401-1.059811-0.621019-1.056702